Motor vehicle accidents account for thousands of deaths and injuries each year. In the United States, traffic collision related deaths exceeded 35,000 in 2015. Worldwide, the estimated annual number of deaths from motor vehicle collisions has exceeded 1,000,000 in recent years. Moreover, collisions cause injury and disability as well as financial costs to both the individuals involved and society.
A number of factors contribute to the risk of collision, including vehicle design, speed of operation, road design, environment, impairment due to alcohol or drugs, and driver skill/behavior. Studies of crash data have found that driver error, intoxication and other human factors contribute wholly or partly to as much as 93% of crashes.
Driver health, in particular, cardiac events such as Sudden Cardiac Death (SCD) leads to a significant number of vehicle accidents each year. Heart disease is rampant in industrialized nations and the number of heart attacks experienced while driving has increased in recent years. Heart disease is the most common cause of natural death in the U.S. and many other industrialized countries. Further, people are spending more time driving and often drive well into their elder years. These factors have led to an increase in the number of accidents that result from cardiac events such as myocardial infarction.
Driver distraction is also attributed to a significant percentage of motor vehicle accidents each year. When distracted, a driver loses awareness of his/her driving situation which increases the risk of an accident as well as the distance needed to slow or stop. Recent studies found that distraction and inattention contribute to approximately 80% of crashes or near crashes. As a result, many jurisdictions have enacted laws banning the use of electronic devices such as smart phones while driving.
Distractions are often separated into three distinct groups: visual, manual, and cognitive. Visual distraction involves taking one's eyes off the road. Manual distraction, also referred to as biomechanical distraction, involves taking one's hands off the wheel. This might occur when a driver reaches for a cell phone or eats while driving. Cognitive distraction occurs when an one's focus is not directly on the act of driving. Cognitive distraction delays a driver's response to critical events. A fourth type of distraction, auditory distraction, may also be considered. Auditory distraction is caused when sounds prevent a driver from making the best use of his/her hearing. This occurs when, for example, a driver focuses his/her hearing toward a cell phone conversation. All distractions compromise the safety of the driver, passengers, bystanders and those in other vehicles
Improvements in the design of motor vehicles have improved their safety, decreasing the number of fatalities and the severity of injuries in accidents. Modern automobiles typically undergo extensive crash testing to improve their safety. Further, vehicles are typically equipped with multiple air bags to protect occupants in case of an accident. Despite improvements in vehicle design and safety, motor vehicles lack any means of monitoring the status of the driver and his/her attention. Doing so could prevent many motor vehicle accidents and greatly reduce the number of vehicular injuries and deaths. Accordingly, there is a need for a system to monitor a driver's level of attention as well as his/her physical health.
Previous efforts have focused on driver observation in attempt to predict cognitive function. For example, U.S. Pat. No. 9,440,657 describes a system that monitors physiological activity of a driver. The data is analyzed by a computer or smart phone to predict whether a driver is impaired. The system relies on movement of the vehicle and driver to determine irregularities and activate an alert. Similarly, WO 2014/027933 describes a driver awareness detection arrangement that uses physiological data in conjunction with vehicle operation information to determine whether a driver is drowsy or impaired. The systems use conventional cameras to observe a driver and movement of an automobile. However, neither system can detect or predict levels of distraction or cardiac events that the driver may experience.
U.S. patent application Ser. No. 13/486,224 describes a driver state module for interfacing with a vehicle and its surroundings. It includes a frame memory for storing representations of behaviors and an evaluation system for ranking the frames based on goals and rewards. The system also uses conventional cameras and relies on driver behavior to predict driver attention and awareness. However, the system may not effectively detect or predict levels of driver distraction and it does not monitor a driver for cardiac events.
Other systems have demonstrated that eye movements can help gauge the level of distraction experienced by a driver. For example, U.S. Pat. No. 8,981,942 describes a system with an optical sensor that monitors a driver to detect eye blinks, head nods, head rotations, and/or gaze fixations. Another optical sensor can monitor the road ahead of the vehicle to detect lane deviation, movement within the lane and time to collision. The system utilizes the data to predict a driver's state of impairment. However, the system does not analyze the driver's pupil activity and has limited effective use.
Although, these systems and methods monitor some aspects of a driver's health and level of attention, they have shortcomings. Conventional systems cannot predict when a user will get distracted and allow for preventive measures. Further, conventional systems do not exploit advances in pupillometry and are prone to error as they do not effectively account for particular characteristics of an individual or stimuli that one may experience in a driving environment. Moreover, conventional systems are not capable of monitoring a driver's cardiac health and predicting cardiac events.
Accordingly, there is a need for an improved system that monitors the cardiac activity and cognitive function of the driver of a motor vehicle. It should incorporate sensors that effectively monitor a driver's heart, eyes and pupil activity to more accurately determine whether a person is overly distracted or having a cardiac event. It should also be capable of predicting whether a driver is likely to become distracted based on a driver's habits and past incidences. Further, the system should be capable of utilizing external data to predict whether a driver is likely to experience a cardiac event.